Forecasting Compliance Burden : A Data-Driven Approach to Regulatory Cost Estimation

Group 6 Team Members : - Banerjee, Sujoy - Ghosh, Arjun - Kotian, Shubha - Nadeem, Shaheryar - Pal, Saurabh

Problem Description

We will investigate the estimated compliance costs associated with U.S. federal regulations. Specifically, we aim to categorize regulatory compliance burden into low and high cost tiers using a combination of regulatory restrictiveness, complexity, and industry relevance.

Why It’s Interesting

Understanding compliance costs is crucial for businesses, policymakers, and economists alike. High compliance costs can influence a company’s profitability, affect the speed of innovation, and even shape industry trends. Despite the significance, compliance costs are rarely quantified in an accessible way, making it challenging for businesses to prepare and allocate resources effectively. By developing a predictive model to categorize compliance costs, this project offers a data-driven approach to identify the most burdensome regulations, helping stakeholders make informed decisions about compliance strategies and potential regulatory reforms.

Analytics plan: How will you analyze your data? What methods or tools will you use?

For this project, the analytics plan will outline how the RegData U.S. 5.0 dataset will be processed, analyzed, and modeled to predict and categorize compliance costs.

Analytics Plan

  1. Data Preprocessing
    • Feature Engineering
    • Standardization/Normalization
    • Categorical Encoding
  2. Exploratory Data Analysis (EDA)
    • Descriptive Statistics
    • Visualizations
    • Correlation Analysis
  3. Classification Modeling
    • Machine Learning Framework: Use H2O or Keras as specified, focusing on a deep learning-based classification model.
    • Model Selection
    • Hyperparameter Tuning
  4. Evaluation
    • Model Metrics
    • Confusion Matrix
    • Feature Importance
  5. Deployment and Interpretation
    • Compliance Cost Predictions
    • Business Insights
    • Documentation

Evaluation Plan

  1. Model Performance Metrics
    • Accuracy
    • Precision, Recall, and F1-Score
  2. Confusion Matrix
  3. ROC-AUC and Precision-Recall Curves
  4. Cross-Validation
  5. Feature Importance Analysis
  6. Error Analysis
  7. Model Comparison (if applicable)

Summary of Evaluation Process

Tools - Libraries: H2O or Keras for model building, pandas for data manipulation, and matplotlib/seaborn for data visualization.

By using a combination of classification metrics, confusion matrix analysis, cross-validation, and feature importance interpretation, this evaluation plan ensures that the model’s predictions are both accurate and meaningful, providing insights into regulatory compliance costs across different categories. This plan also allows for iterating on the model based on misclassification patterns or feature impact, leading to continuous improvement.